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Sayyareh A, Panahi H. Model Selection Based on Tracking Interval under Unified Hybrid Censored Samples. JIRSS. 2017; 18 (1)
URL: http://jirss.irstat.ir/article-1-382-fa.html
سیاره عبدالرضا، پناهی هانیه. Model Selection Based on Tracking Interval under Unified Hybrid Censored Samples. پژوهشنامه انجمن آمار ایران. 1396; 18 (1)

URL: http://jirss.irstat.ir/article-1-382-fa.html

دکتری دکتر
چکیده:   (987 مشاهده)

The aim of statistical modeling is to identify the model that most closely approximates the underlying process. Akaike information criterion (AIC) is commonly used for model selection but the precise value of AIC has no direct interpretation. In this paper we use a normalization of a difference of Akaike criteria in comparing between the two rival models under unified hybrid censoring scheme. Asymptotic properties of maximum likelihood estimator based on the missing information principle are derived. Also, asymptotic distribution of the normalized difference of AIC’s is obtained and it is used to construct an interval, called tracking interval, for comparing the two competing models. Monte Carlo simulations are performed to examine how the proposed interval works for different censoring schemes. Two real data sets have been analyzed for illustrative purposes. The first is selecting between Weibull
and generalized exponential distributions for main component of spearmint essential oil purification data. The second is the choice between models of the lifetimes of 20 electronic components.


نوع مطالعه: Original Paper | موضوع مقاله: 62Fxx: Parametric inference
دریافت: ۱۳۹۵/۷/۵ | پذیرش: ۱۳۹۶/۶/۱۸ | انتشار: ۱۳۹۶/۶/۱۸

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